TL;DR
GATES introduces a novel deep reinforcement learning approach combining Graph Attention Networks and Evolution Strategy to improve cost-aware dynamic workflow scheduling in cloud computing, effectively handling task dependencies and resource variability.
Contribution
It proposes a new DRL method, GATES, that captures task relationships and VM importance, enhancing scheduling stability and performance in dynamic workflows.
Findings
GATES outperforms several state-of-the-art algorithms in experiments.
It effectively models task dependencies using Graph Attention Networks.
GATES demonstrates stable learning with Evolution Strategy.
Abstract
Cost-aware Dynamic Workflow Scheduling (CADWS) is a key challenge in cloud computing, focusing on devising an effective scheduling policy to efficiently schedule dynamically arriving workflow tasks, represented as Directed Acyclic Graphs (DAG), to suitable virtual machines (VMs). Deep reinforcement learning (DRL) has been widely employed for automated scheduling policy design. However, the performance of DRL is heavily influenced by the design of the problem-tailored policy network and is highly sensitive to hyperparameters and the design of reward feedback. Considering the above-mentioned issues, this study proposes a novel DRL method combining Graph Attention Networks-based policy network and Evolution Strategy, referred to as GATES. The contributions of GATES are summarized as follows: (1) GATES can capture the impact of current task scheduling on subsequent tasks by learning the…
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Taxonomy
MethodsSoftmax · Attention Is All You Need
